Effects of Pseudolite Positioning on DOP in LAAS


In this paper, effects on DOP (Dilution of Precision) due to augmentation of Global Positioning System (GPS) with pseudolites are investigated. For this purpose, a typical Local Area Augmentation System (LAAS) scenario is consi-dered by placing pseudolites in various positions. It is found that only properly located pseudolites can improve the DOP. DOP values with two pseudolites located on either side of the run way are found to be the best. Geometric DOP (max) was found to be nearly 4 due to only GPS and came down to approximately 2 due to augmentation with two pseudolites. Implementation aspects of Bayes and Kalman filters while estimating DOP values are also examined.

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Q. Sultana, D. Sunehra, V. Srinivas and A. Sarma, "Effects of Pseudolite Positioning on DOP in LAAS," Positioning, Vol. 1 No. 1, 2010, pp. 18-26. doi: 10.4236/pos.2010.11003.

1. Introduction

A pseudolite (pseudo-satellite) can be considered as a satellite-on-the-ground that transmits GPS like ranging signals [1]. It transmits a signal with code-phase, carrier phase and data components with the same timing similar to the GPS signal format. Pseudolites were used initially to test the initial GPS user equipment [2]. Pseudolites can be used to augment GPS to enhance its availability, integrity and continuity. In the last few years, investigations into use of pseudolites for general positioning, navigation and precision approach for civil aviation have increased [3,4]. Multiple pseudolites transmitting GPS compatible signals can form a stand-alone positioning system if appropriate data acquisition and processing techniques are used [5,6].

In this paper, effect on Dilution of Precision (DOP) due to the augmentation of GPS with pseudolites, in a typical LAAS scenario, is investigated. DOP indicates the effect of geometry formed due to visible satellites, on the user position accuracy. Bayes filter is implemented to remove some of the errors in GPS signals such as tropospheric error and receiver clock bias error, before estimating DOP values. Data acquired from DL-4plus GPS receiver located at Osmania University, Hyderabad, is used for the analysis. To prove the concept, computer simulated pseudolite locations are used in the analysis. Application of Kalman filter while estimating DOPs is also investigated.

2. Experiment with DL-4plus GPS Receiver

A DL-4plus receiver is set up along with the host computer in Research and Training Unit for Navigational Electronics (NERTU), Osmania University, Hyderabad. A 5 m tower is constructed on the terrace of NERTU building. Receiver antenna is mounted on the tower to establish Line of Sight (LoS) with Satellite Vehicles (SVs), thus reasonably avoiding multipath reflections. Data is acquired continuously on 19th January, 2008 for the analysis. Using ‘Convert4’ software the received data is converted to RINEX (Receiver Independent Exchange) format. Two types of files viz., observation file and navigation file are obtained and analysed. Bancroft algorithm is used to find the preliminary position of the receiver [7]. Effects due to Bayes and Kalman filter while estimating DOP are also examined.

2.1. Number of Visible Satellites with Respect to Local Time

From the data collected on 19th January, 2008, information on number of SVs in view over Hyderabad horizon is extracted. In Figure 1, the number of visible SVs is plotted with respect to local time for the whole day. Data corresponding to epochs at every 10 minutes are considered. It can be observed that the number of SVs is varying from a minimum of 6 to a maximum of 11. Least number of SVs (6) is visible at around 9.6 hrs. Maximum

Figure 1. Local time Vs Number of visible satellites (SVs) on 19th January, 2008.

number of SVs (11) is visible mostly during 14-20 hrs.

2.2. Estimation of User Position Using Bancroft Algorithm

Bancroft algorithm (1985) estimates the preliminary coordinates for a GPS receiver. The algorithm requires ECEF coordinates of 4 or more SVs along with the values of their pseudoranges as input [8]. Figure 2 shows the variations in user position estimate with respect to local time. Variations in latitude are found to be negligible Figure 2(a). Variations in longitude are minimal Figure 2(b). Variations in height are relatively large Figure 2(c). From Figure 2(c) it can be observed that the algorithm gives unstable results for the starting of the day. Hence, standard deviation in height is 53.34 m in the first hour. However, over a period of 23 hours (1:00-24:00 hrs) standard deviation in height is reduced to 20.88 m. This value is in accordance with the value reported elsewhere [9]. Minimum, maximum, mean and standard deviation values of latitude, longitude and height are shown in Table 1. Standard deviations of latitude and longitude are minimal. Standard deviation of height is significant (53.34).

2.2.1 Estimation of User Position Using Kalman Filter

Kalman filter estimates the precise position of the receiver. The preliminary position estimated by the Bancroft algorithm is given as input to the Kalman filter along with the pseudoranges. Details on implementation of Kalman filter and other standard GPS related programs can be found elsewhere [9,10].

Figure 3 shows the variations in user position estimate with respect to local time. Latitude variations are negligible Figure 3(a). Minimum, maximum, mean and standard deviation of latitude, longitude and height are shown in Table 2. Variations in longitude are minimal Figure 3(b). Variations in height are relatively significant Figure 3(c). Standard deviation of latitude and longitude are negligible. However, standard deviation of height is relatively large (75.96).

Conflicts of Interest

The authors declare no conflicts of interest.


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